The retailers' and the insurers' weird and wonderful ways of data analytics

There are also tools, such as SessionCam, Hotjar, and Decibel Insight, that specialise in providing session replays and heat maps which use the same type of data collection method but present the data back to you in an easy to digest way. Let's take a look at how the retail and insurance industry have been utilising their data.

The retailers

It’s been known that some online retailers charge different users different amounts for the same product or service, based on website interactions, or even based on the technology they use; Orbitz Worldwide and Amazon being good examples.

Someone identified as being on a Safari browser may be shown either higher priced products or the same product at a higher price. Being a ‘Safari user’ may have been identified by the retailer’s A/B testing programme, as being less price sensitive, or more inclined to purchase more expensive products. As a result, the retailer can then earn a higher margin whilst maintaining their conversion rate. There is reputational risk with this approach but it has been tried by some retailers.

The personal insurers

Organisations within this sector experience significant injuries with fraud, especially users trying to game the quotation system to get the cheapest quote.

User interactions on pages where they can tweak some of the more subjective details, such as car value, annual mileage, job title, etc. may indicate not just a potentially fraudulent application, but may also indicate a propensity to commit a greater fraud at a later date.

Insurers therefore, may decide to either increase the quote or decline the user dependent upon the interactions they make with the website. Whilst the user could start another session or use another browser to try and get round this, the insurer could already have stored the more permanent user details, such as name and date of birth, and therefore may be able to apply the same premium weighting or decline to a future quote attempt.

In another example from the highly competitive personal insurance industry; users can be identified with a unique user ID, which is passed to both the insurer’s internal quote and policy database, and also to the web analytics platform.

Web interaction data is then extracted from the web analytics platform, with the associated unique user ID, and joined to the internal policy database. Given enough time for the claims book to mature on a batch of policies, the insurer can then analyse the data to understand if any activities and patterns from online behaviours are an indication of a potential accident or claim for the insurer. In the future, this allows the insurer to price the risk more accurately, and as a result produce more money by writing more business at the right price, whilst pushing the riskier customers to their competitors.

These examples show there isn’t necessarily a right and a wrong way to use data, and that data sets may have applicability outside of their immediate field. Web analytics data represents complex human behaviours and can therefore be used for multiple purposes outside of the usual user experience enhancement, bug identification, and ecommerce optimisation practices.

Alexander Rigg is digital analyst at ORM London.

ORM is a digital strategy agency based in London. Its clients include Aviva Investors, RBS, Great Western Railway, M&G and Artemis.

www.ormlondon.com

@ormlondon